# Deep Learning for Automated Classification and Characterization of Amorphous Materials

arXiv: https://arxiv.org/abs/1909.04648 · HTML: https://mlsanity.com/paper/1909.04648/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [Score-based denoising for atomic structure identification](https://mlsanity.com/paper/2212.02421/) (2022)
- [What Do Deep Neural Networks Find in Disordered Structures of Glasses?](https://mlsanity.com/paper/2208.00349/) (2022)
- [Machine learning for structure-property relationships: Scalability and limitations](https://mlsanity.com/paper/2304.05502/) (2023)
- [A probabilistic framework for crystal structure denoising, phase classification, and order parameters](https://mlsanity.com/paper/2512.11077/) (2025)
- [Cross-Temperature Defect Identification in Atomistic Simulations via Multi-Level Domain Alignment](https://mlsanity.com/paper/2608.22074/) (2026)

## Abstract (arXiv)

It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics. In this work, we apply deep learning algorithms to accurately classify amorphous materials and characterize their structural features. Specifically, we show that convolutional neural networks and message passing neural networks can classify two-dimensional liquids and liquid-cooled glasses from molecular dynamics simulations with greater than 0.98 AUC, with no a priori assumptions about local particle relationships, even when the liquids and glasses are prepared at the same inherent structure energy. Furthermore, we demonstrate that message passing neural networks surpass convolutional neural networks in this context in both accuracy and interpretability. We extract a clear interpretation of how message passing neural networks evaluate liquid and glass structures by using a self-attention mechanism. Using this interpretation, we derive three novel structural metrics that accurately characterize glass formation. The methods presented here provide us with a procedure to identify important structural features in materials that could be missed by standard techniques and give us a unique insight into how these neural networks process data.